Interference classification with minimal or incomplete information

ABSTRACT

Interference classification with minimal or incomplete information. Receivers in access points and in other network devices on a wireless digital network may be switched to a spectrum monitor mode in which they provide amplitude-versus-frequency information for a chosen part of the spectrum. This may be performed by performing a FFT or similar transform on the signals from the receiver. Receivers are calibrated with known interference sources in controlled environments to determine peaks, pulse frequency, bandwidth, and other identifying parameters of the interference source in best and worst case conditions. These calibrated values are used for matching interference signatures. Calibration is also performed using partial signatures collected over a short period in the order of microseconds. These partial signals may be used to detect interferers while scanning. Another aspect of the invention is to record the variation of noise floor in the presence of interference sources. Multiple interference sources may be detected. While data collection is performed in one or more APs, classification may be performed in the AP or on other systems associated with the network collecting and processing spectrum information from one or more APs.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority on U.S. ProvisionalPatent Application No. 61/299,845 filed Jan. 29, 2010 and entitled“Interference Classification with Minimal or Incomplete Information”.

BACKGROUND OF THE INVENTION

The present invention relates to wireless networks, and in particular,to the problem of classifying interference sources using incomplete orminimal information.

Wireless digital networks are becoming ubiquitous in enterprises,providing secure and cost-effective access to resources. In operation, aplurality of access points (APs) advertise and provide wireless servicesto wireless clients. Client devices may range from the traditional suchas laptop and other portable computers, to dual-mode phones, wirelessdata entry devices and scanners, wireless data acquisition systems, andmore.

These wireless networks operate using frequencies and power levelsassigned by regulatory agencies; the set of frequencies and power levelsvaries from regulatory domain to regulatory domain. The most commonbands for wireless networks include frequency ranges around 2.4 GHz and5 GHz.

In most regulatory domains, wireless networking systems are not thesole, or even primary users of a frequency band such as the 2.4 GHzband. As an example, in the United States, wireless networking devicesoperating to IEEE 802.11 standards are secondary users of the 2.4 GHzISM band, operating under Part 15 of the FCC rules. As Part 15 devices,they must tolerate any interference from other authorized devices on theband. Those other devices on the band include microwave ovens, Bluetoothdevices, a myriad of cordless phones, video and audio devices, and more.Additionally, Amateur Radio operators are authorized to use a portion ofthe 2.4 GHz band, covering channels 1 through 6, at power levels up to akilowatt.

Wireless networks must live with, and adapt to such conditions.Detecting and dealing with interference from another wireless network isa fairly simple process; scan the available channels listening for othernetworks, and pick the channel which offers the best performance giventhe other users of the band.

For interference sources which are not other network devices, however,the process is not as simple. The first step is to classify non-networkinterference sources. Is the source intermittent or continuous? Whatfrequency or frequencies does it cover? At what power levels? Are theeffects localized to one or a small number of access points?

An additional challenge is to make these determinations using thewireless receivers present in devices such as network access points,laptops and other computing devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be best understood by referring to the followingdescription and accompanying drawings that are used to illustrateembodiments of the invention in which:

FIG. 1 shows a wireless network.

FIG. 2 shows an example method of classifying signals received by areceiver connected to a digital network.

DETAILED DESCRIPTION

Embodiments of the invention relate to methods of classifyinginterference to a digital network. While the invention is describedherein in terms of IEEE 802.11 wireless networks, the techniques areequally applicable to other digital systems and networks including butnot limited to WiMAX, Bluetooth, and 3G/4G.

According to the invention, wireless receivers such as access points andclients on a wireless digital network may be switched to a spectrummonitor mode in which they provide amplitude-versus-frequencyinformation for a chosen part of the spectrum. This may be performed byperforming a FFT or similar transform on the received signals. Wirelessreceivers are calibrated with known interference sources in controlledenvironments. The calibration process captures the interferencesignatures as seen by a particular type of receiver and the signaturesmay include relative peak amplitudes, peak to average ratio, centerfrequency, pulse repetition frequency, hopping pattern, bandwidth, andother identifying parameters of the interference source in best andworst case conditions. These calibrated values are used for matchinginterference signatures captured during the operation of the spectrummonitor. Calibration is also performed using partial signaturescollected over a short period in the order of microseconds. Thesepartial signals may be used to detect interferers while scanningmultiple channels. Another aspect of the invention is to record thevariation of noise floor in the presence of interference sources on theoperating channel as well as adjacent channels.

FIG. 1 shows a network in which controller 100 connects to wired network110. In one embodiment of the invention, controller 100 supportsmultiple access points (AP) 120. Also present are interference sources200.

As is known to the art, controller 100 and access points 120 arepurpose-built digital devices, each containing a processor, memoryhierarchy, and input/output interfaces. Processors used generallyinclude MIPS class processors, as well as processors from companies suchas Cavium, Intel, AMD, and Acorn. The memory hierarchy typicallyincludes fast read-write memory such as DRAM for device operation, andnon-volatile memory such as Flash for file storage and device startup.Controller 100 and access points 120 typically operate under the controlof an operating system such as Linux or other real-time capable system.

Controller 100 typically has a plurality of wired interfaces, such asIEEE 802.3 Ethernet interfaces. Access points 120 typically have atleast one wired interface, such as an IEEE 802.3 wired Ethernetinterface, and contain receivers for receiving signals, such as IEEE802.11 wireless signals. Multiple receivers may be present, for the sameband, or for multiple bands. As an example, receivers may be providedfor both 2.4 GHz and 5 GHz bands. These may be provided in the form ofradio modules containing receivers, transmitters, andmodulation/demodulation subsystems.

According to the invention, receivers in Access Points (APs) 120 and inother network devices may be operated in a first, normal mode, and asecond, spectrum monitor mode. These modes may be combined to provide ahybrid device that can support both Access Point and Spectrum Monitorfunctions. In this second, spectrum monitor mode, in addition toreceiving and decoding signals on a selected channel and bandwidth, thereceiver also collects amplitude-versus-frequency information for theselected channel. This may be done, for example, by performing a fastFourier transform (FFT) or other suitable transform on the receiversignal. The output of this spectrum monitor mode is a set of amplitudemeasurements for different frequency bins comprising the channel.

According to the invention, a receiver is calibrated with knowninterfering sources such as a video bridge, Bluetooth, microwave ovensand cordless phones on all channel widths (e.g., 20 MHz and 40 MHz).This is necessary because the exact waveform (peak amplitude, bandwidth,center frequency, etc) that will be seen by an wireless radio such asthose based on IEEE 802.11 for the same interference source is oftendifferent from that of a traditional spectrum analyzer. Since thereceive filters in IEEE 802.11 radios are not quite as sophisticated asthose present in a spectrum analyzer, the structure of the waveformsseen by the IEEE 802.11 radio in an AP can be quite different. Inaddition, the receive channel size (20 MHz or 40 MHz or 80 MHz) alsoaffects the shape of the waveforms. Therefore, the calibrated referencewaveform will be used as a basis to match the interference signatures.The calibrated waveforms are further refined by initial calibration ofthe radio using a known uniform waveform or signal generator. Signalgenerator calibration provides the expected difference between the knownsource and the waveform seen by the IEEE 802.11 radio's FFT engine. Thiscalibration is also performed on the entire receive chain, from theantenna through the receiver. The expected difference between the sourcewaveform and detected waveform is used to create the offsets (frequency,bandwidth, and amplitude) used to match the signatures of other knowninterferers.

This calibration process may be repeated for each different type ofreceiver to be used as a spectrum monitor, and may be repeated fordifferent antenna types as well.

Another aspect of calibration is to determine the partial signatures ofinterferers on a single IEEE 802.11 channel of specific width that canbe detected within a small time interval, which is usually inmicroseconds or milliseconds. If the receiver were to stay on thechannel for a longer period of time it should be able to capture theentire signature patterns of the interferers and match it against thestored pattern. In an AP-based 802.11 spectrum analyzer, the receivercannot stay on the channel for long since it has to scan the entireWi-Fi spectrum. The calibrated partial signature information is used todetect interferers while scanning. In order to accomplish this, the802.11 receiver calibrates with each known interferer, while constantlyscanning (i.e., moving across channels) and stores incomplete spectralsignatures for each type of interferer. During normal operation theincomplete signatures are used to match the interferers.

Another aspect of calibration according to the invention is to recordthe variation of noise floor in the presence of interferers. The 802.11receiver is designed to calibrate its noise floor value when there is noactivity on the channel (quiet time). Finding a quiet time may notalways be possible when there are interferes that remains ON for alonger duration than the noise floor process's timeout, includingconstant interference sources such as audio or video bridges that willbe transmitting almost 100% of the time (i.e., with higher duty cycle).The calibrated noise floor information is used to calculate the actualsignal strength of each FFT bin since the FFT samples only provide theSignal to Noise Ratio for each bin. This process allows the classifierto accurately determine the characteristics of the interferer, includingthe signal strength, bandwidth and duty cycle. Another important use ofthis noise floor is to detect and prevent spurious false detections. Insome noisy or dense environments the actual noise floor may be higherthan the nominal noise floor. In these environments, the classificationalgorithm may incorrectly identify devices due to the increase in noisefloor in the frequency bins of interest. This is typically one of thereasons for false detections for interferers such as microwave ovens.Since the noise floor variations for each type of interferer from thecalibrated information are known, the classification algorithm accordingto the invention will correctly identify this as a false detection sincethe increase in noise floor is due to actual noise on the channel andnot due to the presence of this particular type of interferer.

Yet another aspect of calibration according to the invention is torecord the signatures of multiple interferers that are activesimultaneously. The signatures need to be carefully calibrated toproperly detect the presence of multiple interferers. Without suchcalibration, some interferers could not be detected reliably whenanother interferer is preset or could lead to false detections.

Since only a limited set of options for scanning available channels isavailable (e.g., 20 MHz and 40 MHz), scanning has to take into accountmultiple competing goals. In order to get complete classification data,it is preferable for the radio to stay on a specific channel as long aspossible. The same is true for estimation of the actual duty cycle on achannel. However, the spectrum analysis features require full-spectrumvisibility, which may include both 2.4 GHz (about 100 MHz of spectrum)and 5 GHz (about 500 MHz), including the ability to classify interfererson any part of the spectrum. Therefore, the entire spectrum needs to bescanned at least once during a specific interval of interest. Theinterval of interest could be a few 10 s of milliseconds to a fewseconds.

In addition to interferer classification, the 802.11 based spectrumanalyzer is also required to support detection and classification of all802.11 devices (such as rogues, authorized/unauthorized APs/clients).Therefore, the underlying scanning method needs to supportclassification of interferers and 802.11 devices.

In order to detect all possible 802.11 devices scanning of all possiblechannel widths is required. For example, scanning only 40 MHz or 80 MHzchannels may not be able to detect all 802.11 devices operating in 20MHz channels. In addition, some rogue, unauthorized or misconfigured802.11 devices may operate on a “non-standard” channel. In order to scanthe entire spectrum (e.g., 500 MHz or more) within a specific timeinterval (e.g., 500 ms or one second), scanning all 20 MHz channels isnot practical since classification requires that the radio spend areasonable amount of time on each channel. Scanning according to theinvention uses known as well as dynamically obtained information tointelligently decide the number and type (e.g., channel width) ofchannels to scan and the amount of time to stay on a channel (i.e.,sweep time).

The detection history of 802.11 devices is one of the factors indetermining scanning frequency and sweep time. For example, althoughthere are 14 possible 20 MHz channels in 2.4 GHz spectrum, thesechannels overlap. Some channels are preferred over others depending ondetected activity, history and known deployment patterns. However, allchannels are scanned at least once so as not to miss any potentialinterference sources or 802.11 devices on that channel. The detection ofinterferer on a specific channel will also determine how often to visitthe affected channels, depending on the type of interferers.

Interferer characteristics and known channels of operation of theinterferers are further used to optimize the scanning frequency andsweep time. For example, Microwave Ovens affect a specific set ofchannels in the 2.4 GHz band. Similarly, licensed cordless phones in the5 GHz band typically operate in the upper 5 Ghz band. The channelscanning algorithm takes this information into account while determiningthe order of channel scanning, how often to visit a channel and how longto remain on a channel.

According to the invention, an estimation algorithm is used toreconstruct incomplete signals and classify the interferer when theinterference signature is interleaved with another stronger interfereror 802.11 signals. Since the wireless receiver may be configured toreceive the strongest signals and/or to give preference to 802.11signals, a relatively weaker interference signature may only bepartially received while the stronger or preferred (e.g., 802.11) signaloverlaps the interferer in time. In this case, the wireless receiver maystart to receive the interference signature and then switch to receivingthe stronger or preferred signal. Depending on the duration of theinitial interferer signal, the rest of this signal may be received afterthe completion of the stronger or preferred signal. Based on theincomplete matching of the interference signatures, timestampsassociated with the initial signature and the stronger or preferredsignal and the rest of the interference signature if any, the estimationalgorithm reconstructs the entire interference signature duration toproperly detect the interferer.

There are many challenges in detecting the presence of multipleinterferers on the same channel. When there are more than one type ofinterferer and at least one of the interferers has relatively higherduty cycle, given the short sweep time requirements of the spectrumanalysis function, it would be difficult to get distinct signatures ofeach interferer. Since we have calibrated information about suchmultiple interferers, the classification algorithm looks for suchcombinations in addition to individual signatures to determine ifmultiple interferers are present.

When there is more than one interferer of the same type, a simpleclassifier may not be able to distinguish the presence of multipleinterferers. An example is the presence of multiple Bluetooth devices inthe 2.4 GHz band. Classification uses interferer specific profilinginformation to determine such multiple interferers, in addition to thesignal strength information. For example, Bluetooth devices or somecordless phones have specific frequency hopping patterns and hoppingfrequencies. When the detected signatures indicate a higher hoppingfrequency, the number of devices is estimated based on the number ofhopping signals detected within the dwell time vs. expected hoppingfrequency for that specific device. The signal strength offers anotherdata point for classification to distinguish the presence of multipledevices of the same type. When there are multiple signatures of the sametype with significant difference in signal strength, it typicallyindicates the presence of multiple devices. Since some devices may bemobile (e.g., someone using Bluetooth may be walking away from the APmaking measurements), classification takes this into account indetermining the time window to be used for determining the presence ofmultiple devices.

Another challenge with non-802.11 device classification is duplicatedetection and correlation of devices detected on multiple channels.Since the wireless receiver cannot decode any of the non-802.11protocols, classification must rely on heuristics to detect theinterferers. Many interfering devices such as Bluetooth, Cordlessphones, audio/video bridges and Microwave Ovens can affect more then onechannel. To make things difficult some devices such as Microwave Ovensmay appear with different signal strengths on different channels.

Classification according to the invention uses multiple techniques todetect duplicates. For frequency hopping devices such as Bluetooth andCordless, the hopping frequency is primarily used to determine duplicatedevices since the signal strength of these devices will be approximatelythe same on all detected channels, with proper noise floor calibration.A device-specific threshold is used to account for any fading effectsthat may result in amplitude fluctuations. For other devices such asMicrowave Ovens and fixed frequency devices, the known center frequencyas well as the calibrated information is used to match partialsignatures from different channels to construct the original signal andavoid duplicate detections. When a center frequency is used, an offsetaround the known center frequencies of discovered devices on differentchannels are used to correlate and identify duplicates.

For Microwave Ovens, the peak can be detected based on known andcalibrated information within a few bins of frequency offset. Since thesignal strength on the adjacent channel will be less than the peak, thesignals on multiple channels are stitched together to reconstruct theoriginal Microwave Oven signal and detect duplicates.

Once a device is detected by the classification process, the calculationof the bandwidth and duty-cycle of the interfering device are necessaryfor the purpose of notification to the user as well as for RF managementpurposes.

For known interference sources, the bandwidth and duty-cycle aredetermined based on the classification and sub-classification. Forexample, once Bluetooth is detected, the type of Bluetooth device orhopping pattern is determined (e.g., stereo headset vs. mouse, regularvs. adaptive hopping). Once the sub-type is determined, the estimatedbandwidth and duty-cycle are calculated. This is accomplished byextrapolating the bandwidth and duty-cycle based on the dwell-time. Thenthe estimated values are compared with the known values for thatspecific device. When the difference between the estimated and knownvalues is within a specific threshold, the known values are used;otherwise the estimated values are used. Similar techniques are used toestimate the bandwidth and duty-cycle of devices such as MicrowaveOvens.

For unknown devices and other fixed frequency devices, extrapolation isused to detect the bandwidth and duty-cycle.

While the process of collecting spectrum samples must take place at thewireless receiver such as an AP, analysis can take place at the wirelessreceiver, at a controller hosting a plurality of wireless receivers suchas APs, or by a process running on a dedicated host, or a processrunning on a client device connected to the network. As an example, inone embodiment of the invention, spectrum samples are collected from oneor more APs in the network by a process running on a dedicated host.This process periodically scans for interference sources, or initiates ascan for interference sources when other monitored aspects of networkoperation, such as error rates, retries, noise floors, channel datarates, and the like change. The dedicated host need not be co-locatedwith the network; it may be present at a remote location such as at acorporate monitoring site, as long as connectivity is provided.

In another embodiment of the invention, analysis can be performed by aprocess running on a client device such as a laptop computer associatedwith the network, collecting samples from one or more APs.

As is known to the art, the present invention may be realized inhardware, software, or a combination of hardware and software. Thepresent invention may be realized in a centralized fashion in onecomputer system, or in a distributed fashion where different elementsare spread across several interconnected computer systems associatedwith the network. Any kind of computer system or other apparatus adaptedfor carrying out the methods described herein is suited. A typicalcombination of hardware and software may be a general purpose computersystem with a computer program that, when being loaded and executed,controls the computer system such that it carries out the methodsdescribed herein.

The present invention also may be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

This invention may be embodied in other forms without departing from thespirit or essential attributes thereof. Accordingly, reference should bemade to the following claims, rather than to the foregoingspecification, as indicating the scope of the invention.

FIG. 2 shows an example method of classifying signals received by areceiver connected to a digital network. The example method includes theoperations of calibrating the receiver and generating a library ofsignatures for the receiver (Operation 202), receiving a signal at thereceiver (Operation 204), and classifying the signal by matching thesignal to one or more signatures of the library of signatures (Operation206).

I claim:
 1. The method of classifying signals received by a receiverconnected to a digital network comprising: calibrating the receiver andgenerating a library of signatures for the receiver, receiving a signalat the receiver, and classifying the signal by matching the signal toone or more signatures of the library of signatures.
 2. The method ofclaim 1 where the receiver is switched between a first mode in which thereceiver is used as part of a wireless access point and a second mode inwhich the receiver is used to classify signals.
 3. The method of claim 2where the receiver is switched between the first mode and the secondmode remaining on the same receive frequency.
 4. The method of claim 2where the receiver is switched between the first mode on a firstfrequency and the second mode on a second frequency.
 5. The method ofclaim 1 where the receiver is part of a dedicated monitoring device. 6.The method of claim 1 where the receiver is part of a wireless clientdevice.
 7. The method of claim 1 where the step of calibrating thereceiver and generating a library of signatures comprises: exposing thereceiver to one or more known signal sources, and recording a signaturefor the one or more known signal sources.
 8. The method of claim 7 whereone of the known signal sources is a signal generator.
 9. The method ofclaim 7 where one of the known signal sources is a microwave oven. 10.The method of claim 7 where one of the known signal sources is afrequency-hopping device.
 11. The method of claim 7 where the signaturerecorded is a partial signature.
 12. The method of claim 1 where thestep of calibrating the receiver includes recording the variation ofreceiver noise floor in the presence of one or more signal sources andrecording that variation in the signature.
 13. The method of claim 1where the step of receiving a signal at the receiver further includesusing an estimation algorithm to reconstruct incomplete signals.
 14. Themethod of claim 1 where the step of classifying the signal by matchingthe signal to one or more signatures of the library of signaturescomprises: matching the signal against a single signature.
 15. Themethod of claim 1 where the step of classifying the signal by matchingthe signal to one or more signatures of the library of signaturescomprises: matching the signal against a combination of signatures. 16.The method of claim 1 where the step of classifying the signal bymatching the signal to one or more signatures of the library ofsignatures comprises: matching the signal against a combination ofsignatures of the same type.
 17. The method of claim 1 where the step ofclassifying the signal further includes detecting a signal on multiplechannels.
 18. The method of claim 17 where the step of detecting asignal on multiple channels uses signature information containinghopping patterns of known frequency hopping devices.
 19. The method ofclaim 17 where the frequency hopping device is a Bluetooth device. 20.The method of claim 17 where the frequency hopping device is a cordlessphone.
 21. The method of claim 17 where the step of detecting a signalon multiple channels uses signature information on the central frequencyand power distribution over the bandwidth of a signal.
 22. The method ofclaim 21 where the signal is generated by a microwave oven.
 23. Themethod of claim 1 where the steps of receiving a signal at the receiver,and classifying the signal by matching the signal to one or moresignatures of the library of signatures take place in the receiver. 24.The method of claim 1 where the step of matching the signal to one ormore signatures of the library of signatures further comprises:transferring a representation of the signal to a device connected to thedigital network, and matching the signal to one or more signatures ofthe library of signatures on the device connected to the digitalnetwork.
 25. The method of claim 24 where the device connected to thedigital network is a controller supporting a plurality of receivers. 26.The method of claim 1 where the step of receiving a signal furthercomprises; scanning a combination of channels to cover a frequency band.27. The method of claim 26 further comprising modifying the combinationof channels scanned to cover the frequency band and the channels withinthe band.
 28. The method of claim 26 further comprising modifying thecombination of channels scanned based on dynamically obtainedinformation to determine scan width and sweep time.
 29. The method ofclaim 26 further comprising selecting channels, channel width, and sweeptime based on past detection history.
 30. The method of claim 29 wherethe detection history includes the detection history of 802.11 devices.31. The method of claim 26 further comprising selecting channels,channel width, and sweep times based on known deployment patterns andconfigured channels.